Several points deserve elaboration. Perhaps most surprising to those unfamiliar with MIO based machine learning, is that MIOSR is often faster as the amount of data increases, sometimes significantly [1]}. This is partially due to the fact that the final optimization problem has no dependence on \(n\) , as we only require a one time \(n \times D\)  matrix multiplication to initially construct the objective value coefficients. On the other hand, for small \(n\) , there are more ways to fit the data, so the bounds in the branch-and-bound tree are weaker, necessitating more node exploration. Combining these results with those from Section REF , we conclude that either MIOSR takes a comparable amount of time while achieving the same accuracy, or it takes longer but the extra computational cost buys extra statistical performance. That is, regardless of data size, the cost of MIOSR is justified.
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